Greatest challenge I find is teaching the students about uncertainty in climate projections. I think it's important for students to understand what is an appropriate kind of question to ask a given type (complexity) of model.

My challenge is preconceived attitudes students may have about climate change especially in my general education class. Many of the students at LSU have family and friends that work in the oil industry thus are sensitive to the subject of human induced climate change. My challenge in the classroom includes presenting the data without bias, explaining the science, and educating them on how scientists conduct their research so they can make their own conclusions.
I think it is important for students to understand the differences between a weather forecast and climate models. I heard quite often, "How can scientists predict the consequences of global warming if they cannot predict the weather next week?"

Re: Mea's comment - I think teaching students about the uncertainty in any aspect of science is difficult. Some students come in with the idea that science is a set of unquestionable facts. If there is uncertainty, how can it be science? I now have a 'what is science?' spiel that I bring into most of my classes. I introduce models as a means of testing hypotheses.
If students have a clearer understanding of what science is, it may be easier to separate the politics of climate change from the science of climate change.

This post was editted by Elizabeth Gordon on Oct, 2010
One of the greatest challenges that I've encountered is to get through the barrier of preconceptions. Students seem to gain most of their (mis)information from television, internet, and/or parents, and those sources often have greater credibility than educators. I am hoping that by asking students to work with real data and have them draw their own conclusions, this will help to replace misconceptions - but it's difficult to dislodge information once ingrained. The next challenge for many of my students is to understand how to deal with real data. Many students who take my introductory courses cannot do simple math, and they are overwhelmed when looking at simple graphs (e.g., CO2 concentrations with time). I would like to develop new strategies for getting them to the finish line without them getting side tracked by their limited math skills.

I agree with Mea that uncertainty in climate models (or any data) is an important but difficult concept for teaching climate. One thing that came out of the first talk, and that I think is important to present to students, is how credible these models are - how well they can reproduce observed data, even with uncertainties considered. I was wondering if there is a way to create a 'bad' model output to compare to a 'good' model output to allow students to see the difference. With their lack of familiarity working with real data, I don't think students have the experience to decide what a good fit is - most of the science and math that they've likely encountered in high school is neatly packaged, with nice trends that are made to be obvious. Perhaps by allowing students to see a model that clearly does not fit, and then one that would be considered 'good' in the science community (even if not every data point matches perfectly, which I think is what students expect), and overlay them with the observed data, they can decide on their own which model seems to do a better job of reproducing the observed data. I'm not sure - just thinking . . .

I think that it is really difficult to teach students about models because they come to it with such different skill sets. Those with advanced skills often lose interest if you spend too much time on rudimentary skills. And if you don't scaffold the students newer to modeling, then they can easily become overwhelmed and give up.

I think that it is really difficult to teach students about models because they come to it with such different skill sets. Those with advanced skills often lose interest if you spend too much time on rudimentary skills. And if you don't scaffold the students newer to modeling, then they can easily become overwhelmed and give up.

Perhaps this really boils down to having appropriate learning objectives early in the course--I'm hearing that folks are spending time on the process of science, on the concept of model uncertainty, and on combating cognitive dissonance in the classroom. (I've never taught this before, so thank you so much for the insights!) I am going to restructure the beginning of my course to take these head on, probably using the initial activity (a diagnostic concept sketch of how students view climate change, global warming, and what their opinions/preconceptions about it are) in a more structured way, as fodder for class discussion, or as a directive about how to revisit topics through the course.

What about group work in which students present pros and cons of some competing GCMs and identify 'best practices' in climate modeling?

My intro students don't have a good idea what a basic model is. They understand closed systems, pos. & neg. feedbacks- putting gas in a car, putting sugar in a gas tank. But beyond one level of complexity it is very difficult for them to assess what are drivers. Students learn in introductory courses about each of the "cycles"- hydrologic, carbon, sulfur, nitrogen, etc separately- each with its own diagram, but never do they connect how these cycles interact.

Upper division students have somewhat of an idea what is a model, but they don't see as in Mea's comment the difference in uncertainty and not factual- too much popular press. In many of my classes including field methods, labs, etc. I stress sources of error- what are they, how do they effect your results. Sometimes students take this to heart and declare nothing is valid. I like the idea of having a "bad" model to demonstrate. Very nice.

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